In this essay I argue that technologies and techniques used and developed in the fields of Synthetic Ion Channels and Ion-Channel Reconstitution, which have emerged from the fields of supramolecular chemistry and bio-organic chemistry throughout the past 4 decades, can be applied towards the purpose of gradual cellular (and particularly neuronal) replacement to create a new interdisciplinary field that applies such techniques and technologies towards the goal of the indefinite functional restoration of cellular mechanisms and systems, as opposed to their current proposed use of aiding in the elucidation of cellular mechanisms and their underlying principles, and as biosensors.

In earlier essays (see here and here) I identified approaches to the synthesis of non-biological functional equivalents of neuronal components (i.e., ion-channels, ion-pumps, and membrane sections) and their sectional integration with the existing biological neuron — a sort of “physical” emulation, if you will. It has only recently come to my attention that there is an existing field emerging from supramolecular and bio-organic chemistry centered around the design, synthesis, and incorporation/integration of both synthetic/artificial ion channels and artificial bilipid membranes (i.e., lipid bilayer). The potential uses for such channels commonly listed in the literature have nothing to do with life-extension, however, and the field is, to my knowledge, yet to envision the use of replacing our existing neuronal components as they degrade (or before they are able to), rather seeing such uses as aiding in the elucidation of cellular operations and mechanisms and as biosensors. I argue here that the very technologies and techniques that constitute the field (Synthetic Ion Channels & Ion-Channel/Membrane Reconstitution) can be used towards the purposes of indefinite longevity and life-extension through the iterative replacement of cellular constituents (particularly the components comprising our neurons – ion-channels, ion-pumps, sections of bi-lipid membrane, etc.) so as to negate the molecular degradation they would have otherwise eventually undergone.

While I envisioned an electro-mechanical-systems approach in my earlier essays, the field of Synthetic Ion-Channels from the start in the early 1970s applied a molecular approach to the problem of designing molecular systems that produce certain functions according to their chemical composition or structure. Note that this approach corresponds to (or can be categorized under) the passive-physicalist sub-approach of the physicalist-functionalist approach (the broad approach overlying all varieties of physically embodied, “prosthetic” neuronal functional replication) identified in an earlier essay.

The field of synthetic ion channels is also referred to as ion-channel reconstitution, which designates “the solubilization of the membrane, the isolation of the channel protein from the other membrane constituents and the reintroduction of that protein into some form of artificial membrane system that facilitates the measurement of channel function,” and more broadly denotes “the [general] study of ion channel function and can be used to describe the incorporation of intact membrane vesicles, including the protein of interest, into artificial membrane systems that allow the properties of the channel to be investigated” [1]. The field has been active since the 1970s, with experimental successes in the incorporation of functioning synthetic ion channels into biological bilipid membranes and artificial membranes dissimilar in molecular composition and structure to biological analogues underlying supramolecular interactions, ion selectivity, and permeability throughout the 1980s, 1990s, and 2000s. The relevant literature suggests that their proposed use has thus far been limited to the elucidation of ion-channel function and operation, the investigation of their functional and biophysical properties, and to a lesser degree for the purpose of “in-vitro sensing devices to detect the presence of physiologically active substances including antiseptics, antibiotics, neurotransmitters, and others” through the “… transduction of bioelectrical and biochemical events into measurable electrical signals” [2].

Thus my proposal of gradually integrating artificial ion-channels and/or artificial membrane sections for the purpose of indefinite longevity (that is, their use in replacing existing biological neurons towards the aim of gradual substrate replacement, or indeed even in the alternative use of constructing artificial neurons to — rather than replace existing biological neurons — become integrated with existing biological neural networks towards the aim of intelligence amplification and augmentation while assuming functional and experiential continuity with our existing biological nervous system) appears to be novel, while the notion of artificial ion-channels and neuronal membrane systems ion in general had already been conceived (and successfully created/experimentally verified, though presumably not integrated in vivo).

The field of Functionally Restorative Medicine (and the orphan sub-field of whole-brain gradual-substrate replacement, or “physically embodied” brain-emulation, if you like) can take advantage of the decades of experimental progress in this field, incorporating both the technological and methodological infrastructures used in and underlying the field of Ion-Channel Reconstitution and Synthetic/Artificial Ion Channels & Membrane-Systems (and the technologies and methodologies underlying their corresponding experimental-verification and incorporation techniques) for the purpose of indefinite functional restoration via the gradual and iterative replacement of neuronal components (including sections of bilipid membrane, ion channels, and ion pumps) by MEMS (micro-electrocal-mechanical systems) or more likely NEMS (nano-electro-mechanical systems).

The technological and methodological infrastructure underlying this field can be utilized for both the creation of artificial neurons and for the artificial synthesis of normative biological neurons. Much work in the field required artificially synthesizing cellular components (e.g., bilipid membranes) with structural and functional properties as similar to normative biological cells as possible, so that the alternative designs (i.e., dissimilar to the normal structural and functional modalities of biological cells or cellular components) and how they affect and elucidate cellular properties, could be effectively tested. The iterative replacement of either single neurons, or the sectional replacement of neurons with synthesized cellular components (including sections of the bi-lipid membrane, voltage-dependent ion-channels, ligand-dependent ion channels, ion pumps, etc.) is made possible by the large body of work already done in the field. Consequently the technological, methodological, and experimental infrastructures developed for the fields of Synthetic Ion Channels and Ion-Channel/Artificial-Membrane Reconstitution can be utilized for the purpose of (a) iterative replacement and cellular upkeep via biological analogues (or not differing significantly in structure or functional and operational modality to their normal biological counterparts) and/or (b) iterative replacement with non-biological analogues of alternate structural and/or functional modalities.

Rather than sensing when a given component degrades and then replacing it with an artificially-synthesized biological or non-biological analogue, it appears to be much more efficient to determine the projected time it takes for a given component to degrade or otherwise lose functionality, and simply automate the iterative replacement in this fashion, without providing in vivo systems for detecting molecular or structural degradation. This would allow us to achieve both experimental and pragmatic success in such cellular prosthesis sooner, because it doesn’t rely on the complex technological and methodological infrastructure underlying in vivo sensing, especially on the scale of single neuron components like ion-channels, and without causing operational or functional distortion to the components being sensed.

A survey of progress in the field [3] lists several broad design motifs. I will first list the deign motifs falling within the scope of the survey, and the examples it provides. Selections from both papers are meant to show the depth and breadth of the field, rather than to elucidate the specific chemical or kinetic operations under the purview of each design-variety.

For a much more comprehensive, interactive bibliography of papers falling within the field of Synthetic Ion Channels or constituting the historical foundations of the field, see Jon Chui’s online biography here, which charts the developments in this field up until 2011.

First Survey

Unimolecular ion channels:

Examples include (a) synthetic ion channels with oligocrown ionophores, [5] (b) using a-helical peptide scaffolds and rigid push–pull p-octiphenyl scaffolds for the recognition of polarized membranes, [6] and (c) modified varieties of the b-helical scaffold of gramicidin A [7].

Barrel-stave supramolecules:

Examples of this general class falling include voltage-gated synthetic ion channels formed by macrocyclic bolaamphiphiles and rigidrod p-octiphenyl polyols [8].

Examples of this sub-class falling within the scope of this survey include ‘minimalist’ amphiphiles as synthetic ion channels and pores [23], membrane-active ‘smart’ double-chain amphiphiles, expected to form ‘micellar pores’ or self-assemble into ion channels in response to acid or light [24], and double-chain amphiphiles that may form ‘micellar pores’ at the boundary between photopolymerized and host bilayer domains and representative peptide conjugates that may self-assemble into supramolecular pores or exhibit antibiotic activity [25].

Non-peptide macrocycles as hoops:

Examples of this sub-class falling within the scope of this survey include synthetic ion channels formed by non-peptide macrocycles acyclic analogs [26] and peptide macrocycles containing abiotic amino acids [27].

Peptide macrocycles as hoops and staves:

Examples of this sub-class include (a) synthetic ion channels formed by self-assembly of macrocyclic peptides into genuine barrel-hoop motifs that mimic the b-helix of gramicidin A with cyclic ß-sheets. The macrocycles are designed to bind on top of channels and cationic antibiotics (and several analogs) are proposed to form micellar pores in anionic membranes [28]; (b) synthetic carriers, antibiotics (and analogs), and pores (and analogs) formed by macrocyclic peptides with non-natural subunits. Certain macrocycles may act as ß-sheets, possibly as staves of ß-barrel-like pores [29]; (c) bioengineered pores as sensors. Covalent capturing and fragmentations have been observed on the single-molecule level within engineered a-hemolysin pore containing an internal reactive thiol [30].

Summary

Thus even without knowledge of supramolecular or organic chemistry, one can see that a variety of alternate approaches to the creation of synthetic ion channels, and several sub-approaches within each larger ‘design motif’ or broad-approach, not only exist but have been experimentally verified, varietized, and refined.

Second Survey

The following selections [31] illustrate the chemical, structural, and functional varieties of synthetic ions categorized according to whether they are cation-conducting or anion-conducting, respectively. These examples are used to further emphasize the extent of the field, and the number of alternative approaches to synthetic ion-channel design, implementation, integration, and experimental verification already existent. Permission to use all the following selections and figures was obtained from the author of the source.

There are 6 classical design-motifs for synthetic ion-channels, categorized by structure, that are identified within the paper:

“The channel contained “an amphiphilic ion pair consisting of oligoether-carboxylates and mono– (or di-) octadecylammoniumcations. The carboxylates formed the channel core and the cations formed the hydrophobic outer wall, which was embedded in the bilipid membrane with a channel length of about 24 to 30 Å. The resultant ion channel, formed from molecular self-assembly, is cation-selective and voltage-dependent” [34].

“Later, Kokube et al. synthesized another channel comprising of resorcinol-based cyclic tetramer as the building block. The resorcin-[4]-arenemonomer consisted of four long alkyl chains which aggregated to form a dimeric supramolecular structure resembling that of Gramicidin A” [35]. “Gokel et al. had studied [a set of] simple yet fully functional ion channels known as “hydraphiles” [39].

“An example (channel 3) is shown in Figure 1.6, consisting of diaza-18-crown-6 crown ether groups and alkyl chains as side arms and spacers. Channel 3 is capable of transporting protons across the bilayer membrane” [40].

“A covalently bonded macrotetracycle (Figure 1.8) had shown to be about three times more active than Gokel’s ‘hydraphile’ channel, and its amide-containing analogue also showed enhanced activity” [44].

“Inorganic derivative using crown ethers have also been synthesized. Hall et al. synthesized an ion channel consisting of a ferrocene and 4 diaza-18-crown-6 linked by 2 dodecyl chains (Figure 1.9). The ion channel was redox-active as oxidation of the ferrocene caused the compound to switch to an inactive form” [45].

B-STAVES:

“These are more difficult to synthesize [in comparison to unimolecular varieties] because the channel formation usually involves self-assembly via non-covalent interactions” [47].“A cyclic peptide composed of even number of alternating D– and L-amino acids (Figure 1.10) was suggested to form barrel-hoop structure through backbone-backbone hydrogen bonds by De Santis” [49].

“Experimental results have shown that the channel can transport sodium and potassium ions. The channel can also be constructed by the use of direct covalent bonding between the sheets so as to increase the thermodynamic and kinetic stability” [52].

“By attaching peptides to the octiphenyl scaffold, a ß-barrel can be formed via self-assembly through the formation of ß-sheet structures between the peptide chains (Figure 1.13)” [53].

“The same scaffold was used by Matile et al. to mimic the structure of macrolide antibiotic amphotericin B. The channel synthesized was shown to transport cations across the membrane” [54].

“Attaching the electron-poor naphthalene diimide (NDIs) to the same octiphenyl scaffold led to the hoop-stave mismatch during self-assembly that results in a twisted and closed channel conformation (Figure 1.14). Adding the complementary dialkoxynaphthalene (DAN) donor led to the cooperative interactions between NDI and DAN that favors the formation of barrel-stave ion channel.” [57].

MICELLAR

“These aggregate channels are formed by amphotericin involving both sterols and antibiotics arranged in two half-channel sections within the membrane” [58].

“An active form of the compound is the bolaamphiphiles (two-headed amphiphiles). Figure 1.15 shows an example that forms an active channel structure through dimerization or trimerization within the bilayer membrane. Electrochemical studies had shown that the monomer is inactive and the active form involves dimer or larger aggregates” [60].

ANION CONDUCTING CHANNELS:

“A highly active, anion selective, monomeric cyclodextrin-based ion channel was designed by Madhavan et al. (Figure 1.16). Oligoether chains were attached to the primary face of the ß-cyclodextrin head group via amide bonds. The hydrophobic oligoether chains were chosen because they are long enough to span the entire lipid bilayer. The channel was able to select “anions over cations” and “discriminate among halide anions in the order I- > Br- > Cl- (following Hofmeister series)” [61].

“The anion selectivity occurred via the ring of ammonium cations being positioned just beside the cyclodextrin head group, which helped to facilitate anion selectivity. Iodide ions were transported the fastest because the activation barrier to enter the hydrophobic channel core is lower for I- compared to either Br- or Cl-” [62]. “A more specific artificial anion selective ion channel was the chloride selective ion channel synthesized by Gokel. The building block involved a heptapeptide with Proline incorporated (Figure 1.17)” [63].

Cellular Prosthesis: Inklings of a New Interdisciplinary Approach

The paper cites “nanoreactors for catalysis and chemical or biological sensors” and “interdisciplinary uses as nano –filtration membrane, drug or gene delivery vehicles/transporters as well as channel-based antibiotics that may kill bacterial cells preferentially over mammalian cells” as some of the main applications of synthetic ion-channels [65], other than their normative use in elucidating cellular function and operation.

However, I argue that a whole interdisciplinary field and heretofore-unrecognized new approach or sub-field of Functionally Restorative Medicine is possible through taking the technologies and techniques involved in constructing, integrating, and experimentally verifying either (a) non-biological analogues of ion-channels and ion-pumps (thus trans-membrane membrane proteins in general, also sometimes referred to as transport proteins or integral membrane proteins) and membranes (which include normative bilipid membranes, non-lipid membranes and chemically-augmented bilipid membranes), and (b) the artificial synthesis of biological analogues of ion-channels, ion-pumps and membranes, which are structurally and chemically equivalent to naturally-occurring biological components but which are synthesized artificially – and applying such technologies and techniques toward the purpose the gradual replacement of our existing biological neurons constituting our nervous systems – or at least those neuron-populations that comprise the neocortex and prefrontal cortex, and through iterative procedures of gradual replacement thereby achieving indefinite longevity. There is still work to be done in determining the comparative advantages and disadvantages of various structural and functional (i.e., design) motifs, and in the logistics of implanting the iterative replacement or reconstitution of ion-channels, ion-pumps and sections of neuronal membrane in vivo.

The conceptual schemes outlined in Concepts for Functional Replication of Biological Neurons [66], Gradual Neuron Replacement for the Preservation of Subjective-Continuity [67] and Wireless Synapses, Artificial Plasticity, and Neuromodulation [68] would constitute variations on the basic approach underlying this proposed, embryonic interdisciplinary field. Certain approaches within the fields of nanomedicine itself, particularly those approaches that constitute the functional emulation of existing cell-types, such as but not limited to Robert Freitas’s conceptual designs for the functional emulation of the red blood cell (a.k.a. erythrocytes, haematids) [69], i.e., the Resperocyte, itself should be seen as falling under the purview of this new approach, although not all approaches to Nanomedicine (diagnostics, drug-delivery and neuroelectronic interfacing) constitute the physical (i.e. electromechanical, kinetic, and/or molecular physically embodied) and functional emulation of biological cells.

The field of functionally-restorative medicine in general (and of nanomedicine in particular) and the fields of supramolecular and organic chemistry converge here, where these technological, methodological, and experimental infrastructures developed in the fields of Synthetic Ion-Channels and Ion Channel Reconstitution can be employed to develop a new interdisciplinary approach that applies the logic of prosthesis to the cellular and cellular-component (i.e., sub-cellular) scale; same tools, new use. These techniques could be used to iteratively replace the components of our neurons as they degrade, or to replace them with more robust systems that are less susceptible to molecular degradation. Instead of repairing the cellular DNA, RNA, and protein transcription and synthesis machinery, we bypass it completely by configuring and integrating the neuronal components (ion-channels, ion-pumps, and sections of bilipid membrane) directly.

Thus I suggest that theoreticians of nanomedicine look to the large quantity of literature already developed in the emerging fields of synthetic ion-channels and membrane-reconstitution, towards the objective of adapting and applying existing technologies and methodologies to the new purpose of iterative maintenance, upkeep and/or replacement of cellular (and particularly neuronal) constituents with either non-biological analogues or artificially synthesized but chemically/structurally equivalent biological analogues.

This new sub-field of Synthetic Biology needs a name to differentiate it from the other approaches to Functionally Restorative Medicine. I suggest the designation ‘cellular prosthesis’.

References:

[1] Williams (1994)., An introduction to the methods available for ion channel reconstitution. in D.C Ogden Microelectrode techniques, The Plymouth workshop edition, CambridgeCompany of Biologists.

This essay is the seventh chapter in Franco Cortese’s forthcoming e-book, I Shall Not Go Quietly Into That Good Night!: My Quest to Cure Death, published by the Center for Transhumanity. The first six chapters were previously published on The Rational Argumentator under the following titles:

I was planning on using the NEMS already conceptually developed by Robert Freitas for nanosurgery applications (to be supplemented by the use of MEMS if the technological infrastructure was unavailable at the time) to take in vivo recordings of the salient neural metrics and properties needing to be replicated. One novel approach was to design the units with elongated, worm-like bodies, disposing the computational and electromechanical apparatus within the elongated body of the unit. This sacrifices width for length so as to allow the units to fit inside the extra-cellular space between neurons and glial cells as a postulated solution to a lack of sufficient miniaturization. Moreover, if a unit is too large to be used in this way, extending its length by the same proportion would allow it to then operate in the extracellular space, provided that its means of data-measurement itself weren’t so large as to fail to fit inside the extracellular space (the span of ECF between two adjacent neurons for much of the brain is around 200 Angstroms).

I was planning on using the chemical and electrical sensing methodologies already in development for nanosurgery as the technological and methodological infrastructure for the neuronal data-measurement methodology. However, I also explored my own conceptual approaches to data-measurement. This consisted of detecting variation of morphological features in particular, as the schemes for electrical and chemical sensing already extant seemed either sufficiently developed or to be receiving sufficient developmental support and/or funding. One was the use of laser-scanning or more generally radiography (i.e., sonar) to measure and record morphological data. Another was a device that uses a 2D array of depressible members (e.g., solid members attached to a spring or ratchet assembly, which is operatively connected to a means of detecting how much each individual member is depressed—such as but not limited to piezoelectric crystals that produce electricity in response and proportion to applied mechanical strain). The device would be run along the neuronal membrane and the topology of the membrane would be subsequently recorded by the pattern of depression recordings, which are then integrated to provide a topographic map of the neuron (e.g., relative location of integral membrane components to determine morphology—and magnitude of depression to determine emergent topology). This approach could also potentially be used to identify the integral membrane proteins, rather than using electrical or chemical sensing techniques, if the topologies of the respective proteins are sufficiently different as to be detectable by the unit (determined by its degree of precision, which typically is a function of its degree of miniaturization).

The constructional and data-measurement units would also rely on the technological and methodological infrastructure for organization and locomotion that would be used in normative nanosurgery. I conceptually explored such techniques as the use of a propeller, the use of pressure-based methods (i.e., a stream of water acting as jet exhaust would in a rocket), the use of artificial cilia, and the use of tracks that the unit attaches to so as to be moved electromechanically, which decreases computational intensiveness – a measure of required computation per unit time – rather than having a unit compute its relative location so as to perform obstacle-avoidance and not, say, damage in-place biological neurons. Obstacle-avoidance and related concerns are instead negated through the use of tracks that limit the unit’s degrees of freedom—thus preventing it from having to incorporate computational techniques of obstacle-avoidance (and their entailed sensing apparatus). This also decreases the necessary precision (and thus, presumably, the required degree of miniaturization) of the means of locomotion, which would need to be much greater if the unit were to perform real-time obstacle avoidance. Such tracks would be constructed in iterative fashion. The constructional system would analyze the space in front of it to determine if the space was occupied by a neuron terminal or soma, and extrude the tracks iteratively (e.g., add a segment in spaces where it detects the absence of biological material). It would then move along the newly extruded track, progressively extending it through the spaces between neurons as it moves forward.

Non-Distortional in vivo Brain “Scanning”

A novel avenue of enquiry that occurred during this period involves counteracting or taking into account the distortions caused by the data-measurement units on the elements or properties they are measuring and subsequently applying such corrections to the recording data. A unit changes the local environment that it is supposed to be measuring and recording, which becomes problematic. My solution was to test which operations performed by the units have the potential to distort relevant attributes of the neuron or its environment and to build units that compensate for it either physically or computationally.

If we reduce how a recording unit’s operation distorts neuronal behavior into a list of mathematical rules, we can take the recordings and apply mathematical techniques to eliminate or “cancel out” those distortions post-measurement, thus arriving at what would have been the correct data. This approach would work only if the distortions are affecting the recorded data (i.e., changing it in predictable ways), and not if they are affecting the unit’s ability to actually access, measure, or resolve such data.

The second approach applies the method underlying the first approach to the physical environment of the neuron. A unit senses and records the constituents of the area of space immediately adjacent to its edges and mathematically models that “layer”; i.e., if it is meant to detect ionic solutions (in the case of ECF or ICF), then it would measure their concentration and subsequently model ionic diffusion for that layer. It then moves forward, encountering another adjacent “layer” and integrating it with its extant model. By being able to sense iteratively what is immediately adjacent to it, it can model the space it occupies as it travels through that space. It then uses electric or chemical stores to manipulate the electrical and chemical properties of the environment immediately adjacent to its surface, so as to produce the emergent effects of that model (i.e., the properties of the edges of that model and how such properties causally affect/impact adjacent sections of the environment), thus producing the emergent effects that would have been present if the NRU-construction/integration system or data-measuring system hadn’t occupied that space.

The third postulated solution was the use of a grid comprised of a series of hollow recesses placed in front of the sensing/measuring apparatus. The grid is impressed upon the surface of the membrane. Each compartment isolates a given section of the neuronal membrane from the rest. The constituents of each compartment are measured and recorded, most probably via uptake of its constituents and transport to a suitable measuring apparatus. A simple indexing system can keep track of which constituents came from which grid (and thus which region of the membrane they came from). The unit has a chemical store operatively connected to the means of locomotion used to transport the isolated membrane-constituents to the measuring/sensing apparatus. After a given compartment’s constituents are measured and recorded, the system then marks its constituents (determined by measurement and already stored as recordings by this point of the process), takes an equivalent molecule or compound from a chemical inventory, and replaces the substance it removed for measurement with the equivalent substance from its chemical inventory. Once this is accomplished for a given section of membrane, the grid then moves forward, farther into the membrane, leaving the replacement molecules/compounds from the biochemical inventory in the same respective spots as their original counterparts. It does this iteratively, making its way through a neuron and out the other side. This approach is the most speculative, and thus the least likely to be used. It would likely require the use of NEMS, rather than MEMS, as a necessary technological infrastructure, if the approach were to avoid becoming economically prohibitive, because in order for the compartment-constituents to be replaceable after measurement via chemical store, they need to be simple molecules and compounds rather than sections of emergent protein or tissue, which are comparatively harder to artificially synthesize and store in working order.

***

In the next chapter I describe the work done throughout late 2009 on biological/non-biological NRU hybrids, and in early 2010 on one of two new approaches to retaining subjective-continuity through a gradual replacement procedure, both of which are unrelated to concerns of graduality or sufficient functional equivalence between the biological original and the artificial replication-unit.

In early 2008 I was trying to conceptualize a means of applying the logic of gradual replacement to single neurons under the premise that extending the scale of gradual replacement to individual sections of the neuronal membrane and its integral membrane proteins—thus increasing the degree of graduality between replacement sections—would increase the likelihood of subjective-continuity through substrate transfer. I also started moving away from the use of normative nanotechnology as the technological and methodological infrastructure for the NRUs, as it would delay the date at which these systems could be developed and experimentally verified. Instead I started focusing on conceptualizing systems that electromechanically replicate the functional modalities of the small-scale integral-membrane-components of the neuron. I was calling this approach the “active mechanical membrane” to differentiate it from the electro-chemical-mechanical modalities of the nanotech approach. I also started using MEMS rather than NEMS for the underlying technological infrastructure (because MEMS are less restrictive) while identifying NEMS as preferred.

I felt that trying to replicate the metabolic replacement rate in biological neurons should be the ideal to strive for, since we know that subjective-continuity is preserved through the gradual metabolic replacement (a.k.a. molecular-turnover) that occurs in the existing biological brain. My approach was to measure the normal rate of metabolic replacement in existing biological neurons and the scale at which such replacement occurs (i.e., are the sections being replaced metabolically with single molecules, molecular complexes, or whole molecular clusters?). Then, when replacing sections of the membrane with electromechanical functional equivalents, the same ratio of replacement-section size to replacement-time factor would be applied—that is, the time between sectional replacement would be increased in proportion to how much larger the sectional-replacement section/scale is compared to the existing scale of metabolic replacement-sections/scale. Replacement size/scale is defined as the size of the section being replaced—and so would be molecular complexes in the case of normative metabolic replacement. Replacement time is defined as the interval of time between a given section being replaced and a section that it has causal connection with is replaced; in metabolic replacement it is the time interval between a given molecular complex being replaced and an adjacent (or directly-causally-connected) molecular complex being replaced.

I therefore posited the following formula:

Ta = (Sa/Sb)*Tb,

where Sa is the size of the artificial-membrane-replacement sections, Sb is the size of the metabolic replacement sections, Tb is the time interval between the metabolic replacement of two successive metabolic replacement sections, and Ta is the time interval needing to be applied to the comparatively larger artificial-membrane-replacement sections so as to preserve the same replacement-rate factor (and correspondingly the same degree of graduality) that exists in normative metabolic replacement through the process of gradual replacement on the comparatively larger scale of the artificial-membrane sections.

The use of the time-to-scale factor corresponding with normative molecular turnover or “metabolic replacement” follows from the fact that we know subjective-continuity through substrate replacement is successful at this time-to-scale ratio. However, the lack of a non-arbitrarily quantifiable measure of time and the fact that that time is infinitely divisible (i.e., it can be broken down into smaller intervals to an arbitrarily large degree) logically necessitates that the salient variable is not time, but rather causal interaction between co-affective or “causally coupled” components. Interaction between components and the state transitions each component or procedural step undergo are the only viable quantifiable measures of time. Thus, while time is the relevant variable in the above equation, a better (i.e., more methodologically rigorous) variable would be a measure of either (a) the number of causal interactions occurring between co-affective or “adjacent” components within the interval of replacement time Ta, which is synonymous with the frequency of causal interaction; or (b) the number of state-transitions a given component undergoes within the interval of time Ta. While they should be generally correlative, in that state-transitions are facilitated via causal interaction among components, state-transitions may be a better metric because they allow us to quantitatively compare categorically dissimilar types of causal interaction that otherwise couldn’t be summed into a single variable or measure. For example, if one type of molecular interaction has a greater effect on the state-transitions of either component involved (i.e., facilitates a comparatively greater state-transition) than does another type of molecular interaction, then quantifying a measure of causal interactions may be less accurate than quantifying a measure of the magnitude of state-transitions.

In this way the rate of gradual replacement, despite being on a scale larger than normative metabolic replacement, would hypothetically follow the same degree of graduality with which biological metabolic replacement occurs. This was meant to increase the likelihood of subjective-continuity through a substrate-replacement procedure (both because it is necessarily more gradual than gradual replacement of whole individual neurons at a time, and because it preserves the degree of graduality that exists through the normative metabolic replacement that we already undergo).

Replicating Neuronal Membrane and Integral Membrane Components

Thus far there have been 2 main classes of neuron-replication approach identified: informational-functionalist and physical-functionalist, the former corresponding to computational and simulation/emulation approaches and the latter to physically embodied, “prosthetic” approaches.

The physicalist-functionalist approach, however, can at this point be further sub-divided into two sub-classes. The first can be called “cyber-physicalist-functionalist”, which involves controlling the artificial ion-channels and receptor-channels via normative computation (i.e., an internal CPU or controller-circuit) operatively connected to sensors and to the electromechanical actuators and components of the ion and receptor channels (i.e., sensing the presence of an electrochemical gradient or difference in electrochemical potential [equivalent to relative ionic concentration] between the respective sides of a neuronal membrane, and activating the actuators of the artificial channels to either open or remain closed, based upon programmed rules). This sub-class is an example of a cyber-physical system, which designates any system with a high level of connection or interaction between its physical and computational components, itself a class of technology that grew out of embedded systems, which designates any system using embedded computational technology and includes many electronic devices and appliances.

This is one further functional step removed from the second approach, which I was then simply calling the “direct” method, but which would be more accurately called the passive-physicalist-functionalist approach. Electronic systems are differentiated from electric systems by being active (i.e., performing computation or more generally signal-processing), whereas electric systems are passive and aren’t meant to transform (i.e., process) incoming signals (though any computational system’s individual components must at some level be comprised of electric, passive components). Whereas the cyber-physicalist-functionalist sub-class has computational technology controlling its processes, the passive-physicalist-functionalist approach has components emergently constituting a computational device. This consisted of providing the artificial ion-channels with a means of opening in the presence of a given electric potential difference (i.e., voltage) and the receptor-channels with a means of opening in response to the unique attributes of the neurotransmitter it corresponds to (such as chemical bonding as in ligand-based receptors, or alternatively in response to its electrical properties in the same manner – i.e., according to the same operational-modality – as the artificial ion channels), without a CPU correlating the presence of an attribute measured by sensors with the corresponding electromechanical behavior of the membrane needing to be replicated in response thereto. Such passive systems differ from computation in that they only require feedback between components, wherein a system of mechanical, electrical, or electromechanical components is operatively connected so as to produce specific system-states or processes in response to the presence of specific sensed system-states of its environment or itself. An example of this in regards to the present case would be constructing an ionic channel from piezoelectric materials, such that the presence of a certain electrochemical potential induces internal mechanical strain in the material; the spacing, dimensions and quantity of segments would be designed so as to either close or open, respectively, as a single unit when eliciting internal mechanical strain in response to one electrochemical potential while remaining unresponsive (or insufficiently responsive—i.e., not opening all the way) to another electrochemical potential. Biological neurons work in a similarly passive way, in which systems are organized to exhibit specific responses to specific stimuli in basic stimulus-response causal sequences by virtue of their own properties rather than by external control of individual components via CPU.

However, I found the cyber-physicalist approach preferable if it proved to be sufficient due to the ability to reprogram computational systems, which isn’t possible in passive systems without necessitating a reorganization of the component—which itself necessitates an increase in the required technological infrastructure, thereby increasing cost and design-requirements. This limit on reprogramming also imposes a limit on our ability to modify and modulate the operation of the NRUs (which will be necessary to retain the function of neural plasticity—presumably a prerequisite for experiential subjectivity and memory). The cyber-physicalist approach also seemed preferable due to a larger degree of variability in its operation: it would be easier to operatively connect electromechanical membrane components (e.g., ionic channels, ion pumps) to a CPU, and through the CPU to sensors, programming it to elicit a specific sequence of ionic-channel opening and closing in response to specific sensor-states, than it would be to design artificial ionic channels to respond directly to the presence of an electric potential with sufficient precision and accuracy.

In the cyber-physicalist-functionalist approach the membrane material is constructed so as to be (a) electrically insulative, while (b) remaining thin enough to act as a capacitor via the electric potential differential (which is synonymous with voltage) between the two sides of the membrane.

The ion-channel replacement units consisted of electromechanical pores that open for a fixed amount of time in the presence of an ion gradient (a difference in electric potential between the two sides of the membrane); this was to be accomplished electromechanically via a means of sensing membrane depolarization (such as through the use of reference electrodes) connected to a microcircuit (or nanocircuit, hereafter referred to as a CPU) programmed to open the electromechanical ion-channels for a length of time corresponding to the rate of normative biological repolarization (i.e., the time it takes to restore the membrane polarization to the resting-membrane-potential following an action-potential), thus allowing the influx of ions at a rate equal to the biological ion-channels. Likewise sections of the pre-synaptic membrane were to be replaced by a section of inorganic membrane containing units that sense the presence of the neurotransmitter corresponding to the receptor being replaced, which were to be connected to a microcircuit programmed to elicit specific changes (i.e., increase or decrease in ionic permeability, such as through increasing or decreasing the diameter of ion-channels—e.g., through an increase or decrease in electric stimulation of piezoelectric crystals, as described above—or an increase or decrease in the number of open channels) corresponding to the change in postsynaptic potential in the biological membrane resulting from postsynaptic receptor-binding. This requires a bit more technological infrastructure than I anticipated the ion-channels requiring.

While the accurate and active detection of particular types and relative quantities of neurotransmitters is normally ligand-gated, we have a variety of potential, mutually exclusive approaches. For ligand-based receptors, sensing the presence and steepness of electrochemical gradients may not suffice. However, we don’t necessarily have to use ligand-receptor fitting to replicate the functionality of ligand-based receptors. If there is a difference in the charge (i.e., valence) between the neurotransmitter needing to be detected and other neurotransmitters, and the degree of that difference is detectable given the precision of our sensing technologies, then a means of sensing a specific charge may prove sufficient. I developed an alternate method for ligand-based receptor fitting in the event that sensing-electric charge proved insufficient, however. Different chemicals (e.g., neurotransmitters, but also potentially electrolyte solutions) have different volume-to-weight ratios. We equip the artificial-membrane sections with an empty compartment capable of measuring the weight of its contents. Since the volume of the container is already known, this would allow us to identify specific neurotransmitters (or other relevant molecules and compounds) based on their unique weight-to-volume ratio. By operatively connecting the unit’s CPU to this sensor, we can program specific operations (i.e., receptor opens allowing entry for fixed amount of time, or remains closed) in response to the detection of specific neurotransmitters. Though it is unlikely to be necessitated, this method could also work for the detection of specific ions, and thus could work as the operating mechanism underlying the artificial ion-channels as well—though this would probably require higher-precision volume-to-weight comparison than is required for neurotransmitters.

Sectional Integration with Biological Neurons

Integrating replacement-membrane sections with adjacent sections of the existing lipid bilayer membrane becomes a lot less problematic if the scale at which the membrane sections are handled (determined by the size of the replacement membrane sections) is homogenous, as in the case of biological tissues, rather than molecularly heterogeneous—that is, if we are affixing the edges to a biological tissue, rather than to complexes of individual lipid molecules. Reasons for hypothesizing a higher probability for homogeneity at the replacement scale include (a) the ability of experimenters and medical researchers to puncture the neuronal membrane with a micropipette (so as to measure membrane voltage) without rupturing the membrane beyond functionality, and (b) the fact that sodium and potassium ions do not leak through the gaps between the individual bilipid molecules, which would be present if it were heterogeneous at this scale. If we find homogeneity at the scale of sectional replacement, we can use more normative means of affixing the edges of the replacement membrane section with the existing lipid bilayer membrane, such as micromechanical fasteners, adhesive, or fusing via heating or energizing. However, I also developed an approach applicable if the scale of sectional replacement was found to be molecular and thus heterogeneous. We find an intermediate chemical that stably bonds to both the bilipid molecules constituting the membrane and the molecules or compounds constituting the artificial membrane section. Note that if the molecules or compounds constituting either must be energized so as to put them in an abnormal (i.e., unstable) energy state to make them susceptible to bonding, this is fine so long as the energies don’t reach levels damaging to the biological cell (or if such energies could be absorbed prior to impinging upon or otherwise damaging the biological cell). If such an intermediate molecule or compound cannot be found, a second intermediate chemical that stably bonds with two alternate and secondary intermediate molecules (which themselves bond to either the biological membrane or the non-biological membrane section, respectively) can be used. The chances of finding a sequence of chemicals that stably bond (i.e., a given chemical forms stable bonds with the preceding and succeeding chemicals in the sequence) increases in proportion to the number of intermediate chemicals used. Note that it might be possible to apply constant external energization to certain molecules so as to force them to bond in the case that a stable bond cannot be formed, but this would probably be economically prohibitive and potentially dangerous, depending on the levels of energy and energization-precision.

I also worked on the means of constructing and integrating these components in vivo, using MEMS or NEMS. Most of the developments in this regard are described in the next chapter. However, some specific variations on construction procedure were necessitated by the sectional-integration procedure, which I will comment on here. The integration unit would position itself above the membrane section. Using the data acquired by the neuron data-measurement units, which specify the constituents of a given membrane section and assign it a number corresponding to a type of artificial-membrane section in the integration unit’s section-inventory (essentially a store of stacked artificial-membrane-sections). A means of disconnecting a section of lipid bilayer membrane from the biological neuron is depressed. This could be a hollow rectangular compartment with edges that sever the lipid bilayer membrane via force (e.g., edges terminate in blades), energy (e.g., edges terminate in heat elements), or chemical corrosion (e.g., edges coated with or secrete a corrosive substance). The detached section of lipid bilayer membrane is then lifted out and compacted, to be drawn into a separate compartment for storing waste organic materials. The artificial-membrane section is subsequently transported down through the same compartment. Since it is perpendicular to the face of the container, moving the section down through the compartment should force the intra-cellular fluid (which would have presumably leaked into the constructional container’s internal area when the lipid bilayer membrane-section was removed) back into the cell. Once the artificial-membrane section is in place, the preferred integration method is applied.

Sub-neuronal (i.e., sectional) replacement also necessitates that any dynamic patterns of polarization (e.g., an action potential) are continuated during the interval of time between section removal and artificial-section integration. This was to be achieved by chemical sensors (that detect membrane depolarization) operatively connected to actuators that manipulate ionic concentration on the other side of the membrane gap via the release or uptake of ions from biochemical inventories so as to induce membrane depolarization on the opposite side of the membrane gap at the right time. Such techniques as partially freezing the cell so as to slow the rate of membrane depolarization and/or the propagation velocity of action potentials were also considered.

The next chapter describes my continued work in 2008, focusing on (a) the design requirements for replicating the neural plasticity necessary for memory and subjectivity, (b) the active and conscious modulation and modification of neural operation, (c) wireless synaptic transmission, (d) on ways to integrate new neural networks (i.e., mental amplification and augmentation) without disrupting the operation of existing neural networks and regions, and (e) a gradual transition from or intermediary phase between the physical (i.e., prosthetic) approach and the informational (i.e., computational, or mind-uploading proper) approach.

The simplest approach to the functional replication of biological neurons I conceived of during this period involved what is normally called a “black-box” model of a neuron. This was already a concept in the wider brain-emulation community, but I was yet to find out about it. This is even simpler than the mathematically weighted Artificial Neurons discussed in the previous chapter. Rather than emulating or simulating the behavior of a neuron, (i.e, using actual computational—or more generally signal—processing) we (1) determine the range of input values that a neuron responds to, (2) stimulate the neuron at each interval (the number of intervals depending on the precision of the stimulus) within that input-range, and (3) record the corresponding range of outputs.

This reduces the neuron to essentially a look-up-table (or, more formally, an associative array). The input ranges I originally considered (in 2007) consisted of a range of electrical potentials, but later (in 2008) were developed to include different cumulative organizations of specific voltage values (i.e., some inputs activated and others not) and finally the chemical input and outputs of neurons. The black-box approach was eventually seen as being applied to the sub-neuron scale—e.g., to sections of the cellular membrane. This creates a greater degree of functional precision, bringing the functional modality of the black-box NRU-class in greater accordance with the functional modality of biological neurons. (I.e., it is closer to biological neurons because they do in fact process multiple inputs separately, rather than singular cumulative sums at once, as in the previous versions of the black-box approach.) We would also have a higher degree of variability for a given quantity of inputs.

I soon chanced upon literature dealing with MEMS (micro-electro-mechanical systems) and NEMS (nano-electro-mechanical systems), which eventually led me to nanotechnology and its use in nanosurgery in particular. I saw nanotechnology as the preferred technological infrastructure regardless of the approach used; its physical nature (i.e., operational and functional modalities) could facilitate the electrical and chemical processes of the neuron if the physicalist-functionalist (i.e., physically embodied or ‘prosthetic’) approach proved either preferable or required, while the computation required for its normative functioning (regardless of its particular application) assured that it could facilitate the informationalist-functionalist (i.e., computational emulation or simulation) of neurons if that approach proved preferable. This was true of MEMS as well, with the sole exception of not being able to directly synthesize neurotransmitters via mechanosynthesis, instead being limited in this regard to the release of pre-synthesized biochemical inventories. Thus I felt that I was able to work on conceptual development of the methodological and technological infrastructure underlying both (or at least variations to the existing operational modalities of MEMS and NEMS so as to make them suitable for their intended use), without having to definitively choose one technological/methodological infrastructure over the other. Moreover, there could be processes that are reducible to computation, yet still fail to be included in a computational emulation due to our simply failing to discover the principles underlying them. The prosthetic approach had the potential of replicating this aspect by integrating such a process, as it exists in the biological environment, into its own physical operation, and perform iterative maintenance or replacement of the biological process, until such a time as to be able to discover the underlying principles of those processes (which is a prerequisite for discovering how they contribute to the emergent computation occurring in the neuron) and thus for their inclusion in the informationalist-functionalist approach.

Also, I had by this time come across the existing approaches to Mind-Uploading and Whole-Brain Emulation (WBE), including Randal Koene’s minduploading.org, and realized that the notion of immortality through gradually replacing biological neurons with functional equivalents wasn’t strictly my own. I hadn’t yet come across Kurzweil’s thinking in regard to gradual uploading described in The Singularity is Near (where he suggests a similarly nanotechnological approach), and so felt that there was a gap in the extant literature in regard to how the emulated neurons or neural networks were to communicate with existing biological neurons (which is an essential requirement of gradual uploading and thus of any approach meant to facilitate subjective-continuity through substrate replacement). Thus my perceived role changed from the father of this concept to filling in the gaps and inconsistencies in the already-extant approach and in further developing it past its present state. This is another aspect informing my choice to work on and further varietize both the computational and physical-prosthetic approach—because this, along with the artificial-biological neural communication problem, was what I perceived as remaining to be done after discovering WBE.

The anticipated use of MEMS and NEMS in emulating the physical processes of the neurons included first simply electrical potentials, but eventually developed to include the chemical aspects of the neuron as well, in tandem with my increasing understanding of neuroscience. I had by this time come across Drexler’s Engines of Creation, which was my first introduction to antecedent proposals for immortality—specifically his notion of iterative cellular upkeep and repair performed by nanobots. I applied his concept of mechanosynthesis to the NRUs to facilitate the artificial synthesis of neurotransmitters. I eventually realized that the use of pre-synthesized chemical stores of neurotransmitters was a simpler approach that could be implemented via MEMS, thus being more inclusive for not necessitating nanotechnology as a required technological infrastructure. I also soon realized that we could eliminate the need for neurotransmitters completely by recording how specific neurotransmitters affect the nature of membrane-depolarization at the post-synaptic membrane and subsequently encoding this into the post-synaptic NRU (i.e., length and degree of depolarization or hyperpolarization, and possibly the diameter of ion-channels or differential opening of ion-channels—that is, some and not others) and assigning a discrete voltage to each possible neurotransmitter (or emergent pattern of neurotransmitters; salient variables include type, quantity and relative location) such that transmitting that voltage makes the post-synaptic NRU’s controlling-circuit implement the membrane-polarization changes (via changing the number of open artificial-ion-channels, or how long they remain open or closed, or their diameter/porosity) corresponding to the changes in biological post-synaptic membrane depolarization normally caused by that neurotransmitter.

In terms of the enhancement/self-modification side of things, I also realized during this period that mental augmentation (particularly the intensive integration of artificial-neural-networks with the existing brain) increases the efficacy of gradual uploading by decreasing the total portion of your brain occupied by the biological region being replaced—thus effectively making that portion’s temporary operational disconnection from the rest of the brain more negligible to concerns of subjective-continuity.

While I was thinking of the societal implications of self-modification and self-modulation in general, I wasn’t really consciously trying to do active conceptual work (e.g., working on designs for pragmatic technologies and methodologies as I was with limitless-longevity) on this side of the project due to seeing the end of death as being a much more pressing moral imperative than increasing our degree of self-determination. The 100,000 unprecedented calamities that befall humanity every day cannot wait; for these dying fires it is now or neverness.

Virtual Verification Experiments

The various alternative approaches to gradual substrate-replacement were meant to be alternative designs contingent upon various premises for what was needed to replicate functionality while retaining subjective-continuity through gradual replacement. I saw the various embodiments as being narrowed down through empirical validation prior to any whole-brain replication experiments. However, I now see that multiple alternative approaches—based, for example, on computational emulation (informationalist-functionalist) and physical replication (physicalist-functionalist) (these are the two main approaches thus far discussed) would have concurrent appeal to different segments of the population. The physicalist-functionalist approach might appeal to wide numbers of people who, for one metaphysical prescription or another, don’t believe enough in the computational reducibility of mind to bet their lives on it.

These experiments originally consisted of applying sensors to a given biological neuron, and constructing NRUs based on a series of variations on the two main approaches, running each and looking for any functional divergence over time. This is essentially the same approach outlined in the WBE Roadmap, which I was yet to discover at this point, that suggests a validation approach involving experiments done on single neurons before moving on to the organismal emulation of increasingly complex species up to and including the human. My thinking in regard to these experiments evolved over the next few years to also include the some novel approaches that I don’t think have yet been discussed in communities interested in brain-emulation.

An equivalent physical or computational simulation of the biological neuron’s environment is required to verify functional equivalence, as otherwise we wouldn’t be able to distinguish between functional divergence due to an insufficient replication-approach/NRU-design and functional divergence due to difference in either input or operation between the model and the original (caused by insufficiently synchronizing the environmental parameters of the NRU and its corresponding original). Isolating these neurons from their organismal environment allows the necessary fidelity (and thus computational intensity) of the simulation to be minimized by reducing the number of environmental variables affecting the biological neuron during the span of the initial experiments. Moreover, even if this doesn’t give us a perfectly reliable model of the efficacy of functional replication given the amount of environmental variables one expects a neuron belonging to a full brain to have, it is a fair approximator. Some NRU designs might fail in a relatively simple neuronal environment and thus testing all NRU designs using a number of environmental variables similar to the biological brain might be unnecessary (and thus economically prohibitive) given its cost-benefit ratio. And since we need to isolate the neuron to perform any early non-whole-organism experiments (i.e., on individual neurons) at all, having precise control over the number and nature of environmental variables would be relatively easy, as this is already an important part of the methodology used for normative biological experimentation anyways—because lack of control over environmental variables makes for an inconsistent methodology and thus for unreliable data.

And as we increase to the whole-network and eventually organismal level, a similar reduction of the computational requirements of the NRU’s environmental simulation is possible by replacing the inputs or sensory mechanisms (from single photocell to whole organs) with VR-modulated input. The required complexity and thus computational intensity of a sensorially mediated environment can be vastly minimized if the normative sensory environment of the organism is supplanted with a much-simplified VR simulation.

Note that the efficacy of this approach in comparison with the first (reducing actual environmental variables) is hypothetically greater because going from simplified VR version to the original sensorial environment is a difference, not of category, but of degree. Thus a potentially fruitful variation on the first experiment (physical reduction of a biological neuron’s environmental variables) would be not the complete elimination of environmental variables, but rather decreasing the range or degree of deviation in each variable, including all the categories and just reducing their degree.

Anecdotally, one novel modification conceived during this period involves distributing sensors (operatively connected to the sensory areas of the CNS) in the brain itself, so that we can viscerally sense ourselves thinking—the notion of metasensation: a sensorial infinite regress caused by having sensors in the sensory modules of the CNS, essentially allowing one to sense oneself sensing oneself sensing.

Another is a seeming refigurement of David Pearce’s Hedonistic Imperative—namely, the use of active NRU modulation to negate the effects of cell (or, more generally, stimulus-response) desensitization—the fact that the more times we experience something, or indeed even think something, the more it decreases in intensity. I felt that this was what made some of us lose interest in our lovers and become bored by things we once enjoyed. If we were able to stop cell desensitization, we wouldn’t have to needlessly lose experiential amplitude for the things we love.

In the next chapter I will describe the work I did in the first months of 2008, during which I worked almost wholly on conceptual varieties of the physically embodied prosthetic (i.e., physical-functionalist) approach (particularly in gradually replacing subsections of individual neurons to increase how gradual the cumulative procedure is) for several reasons:

The original utility of ‘hedging our bets’ as discussed earlier—developing multiple approaches increases evolutionary diversity; thus, if one approach fails, we have other approaches to try.

I felt the computational side was already largely developed in the work done by others in Whole-Brain Emulation, and thus that I would be benefiting the larger objective of indefinite longevity more by focusing on those areas that were then comparatively less developed.

The perceived benefit of a new approach to subjective-continuity through a substrate-replacement procedure aiming to increase the likelihood of gradual uploading’s success by increasing the procedure’s cumulative degree of graduality. The approach was called Iterative Gradual Replacement and consisted of undergoing several gradual-replacement procedures, wherein the class of NRU used becomes progressively less similar to the operational modality of the original, biological neurons with each iteration; the greater the number of iterations used, the less discontinuous each replacement-phase is in relation to its preceding and succeeding phases. The most basic embodiment of this approach would involve gradual replacement with physical-functionalist (prosthetic) NRUs that in turn are then gradually replaced with informational-physicalist (computational/emulatory) NRUs. My qualms with this approach today stem from the observation that the operational modalities of the physically embodied NRUs seem as discontinuous in relation to the operational modalities of the computational NRUs as the operational modalities of the biological neurons does. The problem seems to result from the lack of an intermediary stage between physical embodiment and computational (or second-order) embodiment.